ReVis: Towards Reusable Image-Based Visualizations with MLLMs

TL;DR

ReVis introduces a human-AI system that enables flexible reuse and customization of image-based visualizations by parsing them into a novel DSL and reproducing them with an interactive interface.

Contribution

The paper presents ReVis, a new approach combining a DSL and MLLM pipeline for parsing and reproducing image-based visualizations, enhancing flexibility and usability.

Findings

ReVis successfully reproduces a variety of complex visualizations.

The system improves visualization reuse and customization efficiency.

User studies show positive feedback on ReVis's effectiveness.

Abstract

Many expressive visualizations are shared online only as bitmap images, making them difficult to redesign or adapt to new data. Reusing such image-based visualizations requires substantial expertise and is often time-consuming, even for experienced visualization practitioners. Existing work on reproducing visualizations often relies on structured SVG or specifications, supports limited visualization types, and offers limited flexibility for customization. To address these challenges, we present ReVis, a human-AI collaboration approach that enables flexible reuse of image-based visualizations. First, a generic Domain-Specific language (DSL) is proposed to model complex visualizations and support both visualization decomposition and reproduction. Then, ReVis employs an MLLM-based pipeline to parse an image-based visualization into the DSL, delineating its core visual structures and data-to-encoding mappings, and further reproduces the visualization from the DSL. Finally, ReVis includes an interactive interface to allow users to upload visualization images, inspect reproduced results, update the underlying data, and customize visual encodings. A gallery of 40 visualizations demonstrates the expressiveness of the DSL, and a quantitative study evaluates the reproduction quality of ReVis on these examples. Two usage scenarios and user interviews with 16 visualization practitioners demonstrate the effectiveness of ReVis.